Exploring Language-Agnostic Speech Representations Using Domain Knowledge for Detecting Alzheimer's Dementia
Zehra Shah, Shiang Qi, Fei Wang, Mahtab Farrokh, Mashrura Tasnim, Eleni Stroulia, Russell Greiner, Manos Plitsis
Abstract
We explore ways to use speech data to screen for indications of Alzheimer’s dementia (AD). In particular, we describe our approach to the ICASSP 2023 Signal Processing Grand Challenge, which involves extrapolating from models learned from English speech samples, to Greek speech samples, to determine which subjects have AD. By using acoustic and linguistic features, inspired by clinical research on AD, our top-performing classification model achieves 69% accuracy in distinguishing AD patients from healthy controls, and our regression model attains an RMSE of 4.8 for inferring cognitive testing scores. These outcomes underscore the potential of our explainable model for detecting cognitive decline in AD patients via speech, and its applicability in clinical settings.
BibTeX
@inproceedings{icassp2023_exploringlanguag,
title = {Exploring Language-Agnostic Speech Representations Using Domain Knowledge for Detecting Alzheimer's Dementia},
author = {Zehra Shah and Shiang Qi and Fei Wang and Mahtab Farrokh and Mashrura Tasnim and Eleni Stroulia and Russell Greiner and Manos Plitsis and Athanasios Katsamanis},
booktitle = {ICASSP 2023},
year = {2023}
}